Overview

Dataset statistics

Number of variables14
Number of observations1127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.4 KiB
Average record size in memory112.1 B

Variable types

Numeric14

Alerts

probability_race_G is highly overall correlated with probability_race_O and 9 other fieldsHigh correlation
probability_race_P is highly overall correlated with ageHigh correlation
probability_race_O is highly overall correlated with probability_race_G and 4 other fieldsHigh correlation
age is highly overall correlated with probability_race_G and 9 other fieldsHigh correlation
partisan_score is highly overall correlated with probability_race_G and 7 other fieldsHigh correlation
turnout_score is highly overall correlated with probability_race_G and 8 other fieldsHigh correlation
probability_highest_education_high_school is highly overall correlated with probability_race_G and 2 other fieldsHigh correlation
support_tax_on_wealthy_score is highly overall correlated with probability_race_G and 8 other fieldsHigh correlation
support_progressive_taxation_score is highly overall correlated with probability_race_G and 6 other fieldsHigh correlation
support_cannabis_legalization_score is highly overall correlated with probability_race_G and 6 other fieldsHigh correlation
probability_income_over_100k is highly overall correlated with probability_race_G and 3 other fieldsHigh correlation
probability_children_in_household is highly overall correlated with ageHigh correlation
support_trump_score is highly overall correlated with probability_race_G and 5 other fieldsHigh correlation
probability_race_G has unique valuesUnique
probability_race_P has unique valuesUnique
probability_race_O has unique valuesUnique
partisan_score has unique valuesUnique
turnout_score has unique valuesUnique
probability_highest_education_high_school has unique valuesUnique
support_tax_on_wealthy_score has unique valuesUnique
support_progressive_taxation_score has unique valuesUnique
support_cannabis_legalization_score has unique valuesUnique
probability_income_over_100k has unique valuesUnique
probability_children_in_household has unique valuesUnique
support_trump_score has unique valuesUnique

Reproduction

Analysis started2023-02-01 05:31:33.287989
Analysis finished2023-02-01 05:31:53.003886
Duration19.72 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

probability_race_G
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.508298
Minimum12.624398
Maximum97.291262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:53.075825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.624398
5-th percentile24.673152
Q160.776786
median76.768773
Q386.092036
95-th percentile93.025686
Maximum97.291262
Range84.666864
Interquartile range (IQR)25.31525

Descriptive statistics

Standard deviation20.932213
Coefficient of variation (CV)0.29687588
Kurtosis0.25417307
Mean70.508298
Median Absolute Deviation (MAD)11.192166
Skewness-1.1018323
Sum79462.852
Variance438.15754
MonotonicityNot monotonic
2023-01-31T23:31:53.181977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.54317351 1
 
0.1%
90.92746792 1
 
0.1%
88.62300076 1
 
0.1%
91.34666667 1
 
0.1%
78.72717391 1
 
0.1%
93.57086614 1
 
0.1%
85.93754487 1
 
0.1%
63.26451613 1
 
0.1%
89.83535282 1
 
0.1%
75.19719626 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
12.62439807 1
0.1%
12.83978398 1
0.1%
12.93936414 1
0.1%
13.11453416 1
0.1%
13.28379287 1
0.1%
13.51158301 1
0.1%
13.64630468 1
0.1%
13.8028169 1
0.1%
13.98593407 1
0.1%
14 1
0.1%
ValueCountFrequency (%)
97.29126214 1
0.1%
96.88888889 1
0.1%
96.58990632 1
0.1%
95.91141869 1
0.1%
95.90935673 1
0.1%
95.87760252 1
0.1%
95.82106455 1
0.1%
95.70247934 1
0.1%
95.52608213 1
0.1%
95.37437722 1
0.1%

probability_race_P
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.641283
Minimum0.17895772
Maximum32.405448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:53.288109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.17895772
5-th percentile1.05667
Q11.7199599
median2.752584
Q34.6618678
95-th percentile9.1991302
Maximum32.405448
Range32.226491
Interquartile range (IQR)2.941908

Descriptive statistics

Standard deviation2.8523887
Coefficient of variation (CV)0.78334714
Kurtosis13.121417
Mean3.641283
Median Absolute Deviation (MAD)1.2267154
Skewness2.6153607
Sum4103.726
Variance8.136121
MonotonicityNot monotonic
2023-01-31T23:31:53.384196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.708756603 1
 
0.1%
1.566116784 1
 
0.1%
1.765422696 1
 
0.1%
2.901333333 1
 
0.1%
0.9380434783 1
 
0.1%
1.318897638 1
 
0.1%
2.640703518 1
 
0.1%
4.457685009 1
 
0.1%
1.684960798 1
 
0.1%
3.646105919 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
0.1789577188 1
0.1%
0.2112760835 1
0.1%
0.2285714286 1
0.1%
0.2594594595 1
0.1%
0.276371308 1
0.1%
0.3333333333 1
0.1%
0.3957983193 1
0.1%
0.4340949033 1
0.1%
0.436376971 1
0.1%
0.4446227929 1
0.1%
ValueCountFrequency (%)
32.4054484 1
0.1%
19.97960445 1
0.1%
18.54166667 1
0.1%
16.88769231 1
0.1%
16.76573321 1
0.1%
16.59680851 1
0.1%
16.55693614 1
0.1%
16.4782005 1
0.1%
15.68670592 1
0.1%
15.4 1
0.1%

probability_race_O
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.03022
Minimum0.359375
Maximum85.301868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:53.487325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.359375
5-th percentile2.0716952
Q16.1507437
median12.042247
Q322.269008
95-th percentile58.422786
Maximum85.301868
Range84.942493
Interquartile range (IQR)16.118264

Descriptive statistics

Standard deviation17.336864
Coefficient of variation (CV)0.96154483
Kurtosis2.210621
Mean18.03022
Median Absolute Deviation (MAD)7.1441754
Skewness1.6466981
Sum20320.058
Variance300.56687
MonotonicityNot monotonic
2023-01-31T23:31:53.583412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.617025599 1
 
0.1%
2.524744698 1
 
0.1%
2.582635187 1
 
0.1%
2.966222222 1
 
0.1%
17.74456522 1
 
0.1%
1.078740157 1
 
0.1%
8.021177315 1
 
0.1%
27.71385199 1
 
0.1%
2.499643621 1
 
0.1%
16.56542056 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
0.359375 1
0.1%
0.6699029126 1
0.1%
0.6812933025 1
0.1%
0.6914393227 1
0.1%
0.7152542373 1
0.1%
0.8156370656 1
0.1%
0.8184263618 1
0.1%
0.8524822695 1
0.1%
0.8567543064 1
0.1%
0.8847884788 1
0.1%
ValueCountFrequency (%)
85.30186824 1
0.1%
84.98394864 1
0.1%
84.52621118 1
0.1%
81.04196495 1
0.1%
79.39015817 1
0.1%
79.021398 1
0.1%
77.88989716 1
0.1%
77.53693694 1
0.1%
77.33277311 1
0.1%
75.44514768 1
0.1%

gender
Real number (ℝ)

Distinct1120
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45401139
Minimum0.30934744
Maximum0.66666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:53.682010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.30934744
5-th percentile0.39703185
Q10.43664365
median0.45317221
Q30.47080722
95-th percentile0.51463334
Maximum0.66666667
Range0.35731922
Interquartile range (IQR)0.034163564

Descriptive statistics

Standard deviation0.035214721
Coefficient of variation (CV)0.077563518
Kurtosis3.7152173
Mean0.45401139
Median Absolute Deviation (MAD)0.017116383
Skewness0.60468517
Sum511.67084
Variance0.0012400766
MonotonicityNot monotonic
2023-01-31T23:31:53.784173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 3
 
0.3%
0.4833333333 2
 
0.2%
0.4049773756 2
 
0.2%
0.4382120947 2
 
0.2%
0.4042553191 2
 
0.2%
0.4820143885 2
 
0.2%
0.4708813454 1
 
0.1%
0.4548387097 1
 
0.1%
0.4581430746 1
 
0.1%
0.4236234458 1
 
0.1%
Other values (1110) 1110
98.5%
ValueCountFrequency (%)
0.3093474427 1
0.1%
0.3333333333 1
0.1%
0.3369489153 1
0.1%
0.3395061728 1
0.1%
0.347826087 1
0.1%
0.3536201469 1
0.1%
0.3560732113 1
0.1%
0.356223176 1
0.1%
0.3644859813 1
0.1%
0.3652571429 1
0.1%
ValueCountFrequency (%)
0.6666666667 1
0.1%
0.6619047619 1
0.1%
0.60844185 1
0.1%
0.578026592 1
0.1%
0.5759493671 1
0.1%
0.5729278794 1
0.1%
0.5729094679 1
0.1%
0.5700123916 1
0.1%
0.5665733707 1
0.1%
0.5659670165 1
0.1%

age
Real number (ℝ)

Distinct1126
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.798158
Minimum21.342016
Maximum80.626814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:53.893289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21.342016
5-th percentile41.189067
Q146.280815
median49.494076
Q354.320727
95-th percentile64.127136
Maximum80.626814
Range59.284798
Interquartile range (IQR)8.0399119

Descriptive statistics

Standard deviation7.2805902
Coefficient of variation (CV)0.1433239
Kurtosis2.0872349
Mean50.798158
Median Absolute Deviation (MAD)3.8815639
Skewness0.91045176
Sum57249.524
Variance53.006994
MonotonicityNot monotonic
2023-01-31T23:31:53.991094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.33333333 2
 
0.2%
60.44761132 1
 
0.1%
51.70349252 1
 
0.1%
65.95648313 1
 
0.1%
60.17806732 1
 
0.1%
56.41338583 1
 
0.1%
53.81899642 1
 
0.1%
49.3343442 1
 
0.1%
54.16810683 1
 
0.1%
42.48820355 1
 
0.1%
Other values (1116) 1116
99.0%
ValueCountFrequency (%)
21.34201586 1
0.1%
22.07744565 1
0.1%
29.71181658 1
0.1%
31.50817499 1
0.1%
32.05602782 1
0.1%
33.42409902 1
0.1%
33.58656873 1
0.1%
35.72340798 1
0.1%
36.49327628 1
0.1%
36.64036885 1
0.1%
ValueCountFrequency (%)
80.6268141 1
0.1%
77.99778024 1
0.1%
76.35649123 1
0.1%
76.27991603 1
0.1%
76.06119951 1
0.1%
75.99611046 1
0.1%
75.95130238 1
0.1%
75.77476341 1
0.1%
75.73183857 1
0.1%
75.6341637 1
0.1%

partisan_score
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.056482
Minimum6.2921348
Maximum93.587279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:54.098034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.2921348
5-th percentile21.567782
Q132.94162
median45.085268
Q364.410875
95-th percentile86.630953
Maximum93.587279
Range87.295144
Interquartile range (IQR)31.469254

Descriptive statistics

Standard deviation20.454471
Coefficient of variation (CV)0.41695756
Kurtosis-0.83939704
Mean49.056482
Median Absolute Deviation (MAD)14.096446
Skewness0.45582798
Sum55286.655
Variance418.38538
MonotonicityNot monotonic
2023-01-31T23:31:54.197159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.12516482 1
 
0.1%
27.11599895 1
 
0.1%
32.01598174 1
 
0.1%
21.11012433 1
 
0.1%
64.94353963 1
 
0.1%
17.36614173 1
 
0.1%
34.56129032 1
 
0.1%
44.70015163 1
 
0.1%
31.61511048 1
 
0.1%
54.10650888 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
6.292134831 1
0.1%
7.333333333 1
0.1%
7.713508613 1
0.1%
10.88815789 1
0.1%
12.24271845 1
0.1%
13.7389581 1
0.1%
13.90104167 1
0.1%
14.21307506 1
0.1%
14.37736666 1
0.1%
16.02948403 1
0.1%
ValueCountFrequency (%)
93.58727929 1
0.1%
92.02262443 1
0.1%
91.77406593 1
0.1%
91.54828151 1
0.1%
91.19945504 1
0.1%
91.13586687 1
0.1%
91.02459954 1
0.1%
90.68150346 1
0.1%
90.5210643 1
0.1%
90.21242775 1
0.1%

turnout_score
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.143227
Minimum35.555027
Maximum87.277936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:54.294756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35.555027
5-th percentile52.543413
Q161.294513
median68.827957
Q374.986673
95-th percentile82.164956
Maximum87.277936
Range51.722909
Interquartile range (IQR)13.69216

Descriptive statistics

Standard deviation9.2012198
Coefficient of variation (CV)0.13502765
Kurtosis-0.40295327
Mean68.143227
Median Absolute Deviation (MAD)6.8746365
Skewness-0.30464754
Sum76797.417
Variance84.662445
MonotonicityNot monotonic
2023-01-31T23:31:54.394372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.11461609 1
 
0.1%
80.03561142 1
 
0.1%
81.19101979 1
 
0.1%
69.19449378 1
 
0.1%
77.95005429 1
 
0.1%
81.14566929 1
 
0.1%
68.82795699 1
 
0.1%
69.41205459 1
 
0.1%
78.90021383 1
 
0.1%
64.7907194 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
35.55502717 1
0.1%
36.64479638 1
0.1%
38.3744204 1
0.1%
38.453125 1
0.1%
43.3902439 1
0.1%
44.05452128 1
0.1%
44.53061224 1
0.1%
44.74691358 1
0.1%
46.90666667 1
0.1%
46.93772727 1
0.1%
ValueCountFrequency (%)
87.2779358 1
0.1%
87.2348378 1
0.1%
87.07242152 1
0.1%
85.80122549 1
0.1%
85.2455516 1
0.1%
85.24386097 1
0.1%
85.24225865 1
0.1%
85.21291209 1
0.1%
85.17746777 1
0.1%
84.97796818 1
0.1%

probability_highest_education_high_school
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.357842
Minimum26.084028
Maximum87.490151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:54.497992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum26.084028
5-th percentile30.617418
Q141.350842
median52.350324
Q363.578821
95-th percentile73.502847
Maximum87.490151
Range61.406122
Interquartile range (IQR)22.227978

Descriptive statistics

Standard deviation13.53457
Coefficient of variation (CV)0.2585013
Kurtosis-1.0543155
Mean52.357842
Median Absolute Deviation (MAD)11.127642
Skewness-0.0050088143
Sum59007.288
Variance183.18459
MonotonicityNot monotonic
2023-01-31T23:31:54.597100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.44354508 1
 
0.1%
29.13336848 1
 
0.1%
28.81880734 1
 
0.1%
72.03880866 1
 
0.1%
58.09554832 1
 
0.1%
39.79841897 1
 
0.1%
52.24059334 1
 
0.1%
56.84365325 1
 
0.1%
27.76217765 1
 
0.1%
50.89713746 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
26.08402822 1
0.1%
26.29037801 1
0.1%
26.4338697 1
0.1%
27.16449561 1
0.1%
27.16666667 1
0.1%
27.37171629 1
0.1%
27.45857988 1
0.1%
27.46582278 1
0.1%
27.53857622 1
0.1%
27.5403752 1
0.1%
ValueCountFrequency (%)
87.49015064 1
0.1%
85.03598616 1
0.1%
79.04618474 1
0.1%
78.32919255 1
0.1%
78.25890279 1
0.1%
77.49498069 1
0.1%
77.45218543 1
0.1%
77.43097643 1
0.1%
76.72222222 1
0.1%
76.67856419 1
0.1%

support_tax_on_wealthy_score
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.343354
Minimum42.637566
Maximum80.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:54.699193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42.637566
5-th percentile51.962857
Q159.567056
median65.967742
Q372.058617
95-th percentile76.161551
Maximum80.92
Range38.282434
Interquartile range (IQR)12.491561

Descriptive statistics

Standard deviation7.772387
Coefficient of variation (CV)0.11894686
Kurtosis-0.70342763
Mean65.343354
Median Absolute Deviation (MAD)6.2182521
Skewness-0.34868281
Sum73641.96
Variance60.409999
MonotonicityNot monotonic
2023-01-31T23:31:54.798283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.11032532 1
 
0.1%
47.8425656 1
 
0.1%
46.24668705 1
 
0.1%
62.7216 1
 
0.1%
73.76271186 1
 
0.1%
47.5320197 1
 
0.1%
65.31188972 1
 
0.1%
67.55948276 1
 
0.1%
49.02746212 1
 
0.1%
72.30413386 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
42.63756614 1
0.1%
43.77981172 1
0.1%
43.8356525 1
0.1%
45.4950495 1
0.1%
45.62985685 1
0.1%
45.89181287 1
0.1%
45.90598841 1
0.1%
45.95423497 1
0.1%
46.11520737 1
0.1%
46.24668705 1
0.1%
ValueCountFrequency (%)
80.92 1
0.1%
80.65811966 1
0.1%
79.82758621 1
0.1%
79.82055749 1
0.1%
79.40029112 1
0.1%
79.26666667 1
0.1%
78.97310127 1
0.1%
78.86900958 1
0.1%
78.69061758 1
0.1%
78.56091954 1
0.1%

support_progressive_taxation_score
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.660809
Minimum16.777778
Maximum84.023923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:54.906382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.777778
5-th percentile31.716729
Q140.934395
median50.069337
Q361.881224
95-th percentile76.354779
Maximum84.023923
Range67.246146
Interquartile range (IQR)20.946829

Descriptive statistics

Standard deviation13.865789
Coefficient of variation (CV)0.26840053
Kurtosis-0.78527212
Mean51.660809
Median Absolute Deviation (MAD)10.060898
Skewness0.28893488
Sum58221.731
Variance192.2601
MonotonicityNot monotonic
2023-01-31T23:31:55.006472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.92704918 1
 
0.1%
36.63073274 1
 
0.1%
36.78134557 1
 
0.1%
32.30685921 1
 
0.1%
61.20955483 1
 
0.1%
24.92094862 1
 
0.1%
41.04377713 1
 
0.1%
51.62113003 1
 
0.1%
40.06303725 1
 
0.1%
53.19345706 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
16.77777778 1
0.1%
19.88888889 1
0.1%
20.10227273 1
0.1%
22.75496689 1
0.1%
24.08312958 1
0.1%
24.38690476 1
0.1%
24.92094862 1
0.1%
25.46048898 1
0.1%
25.4791901 1
0.1%
26.9721223 1
0.1%
ValueCountFrequency (%)
84.02392344 1
0.1%
83.22314675 1
0.1%
82.99650757 1
0.1%
82.65798046 1
0.1%
82.44084137 1
0.1%
82.02605863 1
0.1%
81.92650334 1
0.1%
81.88498623 1
0.1%
81.80501931 1
0.1%
81.74236253 1
0.1%

support_cannabis_legalization_score
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.992244
Minimum31.988636
Maximum84.714203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:55.111568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31.988636
5-th percentile44.236556
Q152.841856
median58.982282
Q364.582489
95-th percentile75.243658
Maximum84.714203
Range52.725566
Interquartile range (IQR)11.740633

Descriptive statistics

Standard deviation9.0885459
Coefficient of variation (CV)0.1540634
Kurtosis-0.14771568
Mean58.992244
Median Absolute Deviation (MAD)5.9160944
Skewness0.084792495
Sum66484.259
Variance82.601667
MonotonicityNot monotonic
2023-01-31T23:31:55.216663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.35297131 1
 
0.1%
52.14232999 1
 
0.1%
46.92889908 1
 
0.1%
44.29241877 1
 
0.1%
65.96634093 1
 
0.1%
39.2687747 1
 
0.1%
52.82054993 1
 
0.1%
59.43188854 1
 
0.1%
52.66690544 1
 
0.1%
60.17709972 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
31.98863636 1
0.1%
35.33333333 1
0.1%
36.26624294 1
0.1%
36.67428571 1
0.1%
36.78472222 1
0.1%
37.46283255 1
0.1%
37.66648045 1
0.1%
38.08707865 1
0.1%
38.14269275 1
0.1%
38.46232439 1
0.1%
ValueCountFrequency (%)
84.71420256 1
0.1%
84.45417515 1
0.1%
83.11043689 1
0.1%
82.0104712 1
0.1%
81.30115274 1
0.1%
81.30027548 1
0.1%
80.94402421 1
0.1%
80.68146718 1
0.1%
80.23311897 1
0.1%
80.10069444 1
0.1%

probability_income_over_100k
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.253141
Minimum20.029652
Maximum86.166667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:55.320267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.029652
5-th percentile23.901653
Q133.574139
median43.952085
Q358.225216
95-th percentile73.603805
Maximum86.166667
Range66.137015
Interquartile range (IQR)24.651077

Descriptive statistics

Standard deviation15.478358
Coefficient of variation (CV)0.33464447
Kurtosis-0.91617045
Mean46.253141
Median Absolute Deviation (MAD)12.020056
Skewness0.35412114
Sum52127.29
Variance239.57956
MonotonicityNot monotonic
2023-01-31T23:31:55.417866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0710041 1
 
0.1%
76.84923564 1
 
0.1%
76.32568807 1
 
0.1%
27.35830325 1
 
0.1%
30.93159609 1
 
0.1%
73.62450593 1
 
0.1%
48.87120116 1
 
0.1%
40.46013932 1
 
0.1%
77.39326648 1
 
0.1%
41.50015728 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
20.02965159 1
0.1%
20.20534224 1
0.1%
20.49708455 1
0.1%
20.65140845 1
0.1%
21.07883817 1
0.1%
21.39846006 1
0.1%
21.44175824 1
0.1%
21.49272727 1
0.1%
21.5 1
0.1%
21.51724138 1
0.1%
ValueCountFrequency (%)
86.16666667 1
0.1%
79.3396488 1
0.1%
78.84413085 1
0.1%
78.81965318 1
0.1%
78.6984127 1
0.1%
78.47639887 1
0.1%
78.46116323 1
0.1%
78.4405416 1
0.1%
78.04937898 1
0.1%
77.97250204 1
0.1%

probability_children_in_household
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.838644
Minimum14.758333
Maximum66.056437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:55.519957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14.758333
5-th percentile30.898231
Q141.478124
median46.784509
Q350.893637
95-th percentile58.83403
Maximum66.056437
Range51.298104
Interquartile range (IQR)9.4155128

Descriptive statistics

Standard deviation8.5668643
Coefficient of variation (CV)0.18689175
Kurtosis1.7395671
Mean45.838644
Median Absolute Deviation (MAD)4.5860941
Skewness-0.86582536
Sum51660.152
Variance73.391163
MonotonicityNot monotonic
2023-01-31T23:31:55.621049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.31844262 1
 
0.1%
47.26357406 1
 
0.1%
42.43577982 1
 
0.1%
31.22292419 1
 
0.1%
34.18892508 1
 
0.1%
40.4743083 1
 
0.1%
43.17040521 1
 
0.1%
54.44388545 1
 
0.1%
48.06375358 1
 
0.1%
42.48191255 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
14.75833333 1
0.1%
15.16359164 1
0.1%
15.87374302 1
0.1%
15.97965571 1
0.1%
16.15183616 1
0.1%
16.59303666 1
0.1%
16.60884277 1
0.1%
16.70235546 1
0.1%
16.86833333 1
0.1%
17.38596491 1
0.1%
ValueCountFrequency (%)
66.05643739 1
0.1%
65.54511949 1
0.1%
65.00319244 1
0.1%
64.92408377 1
0.1%
64.36871345 1
0.1%
63.55739194 1
0.1%
63.54777476 1
0.1%
63.26254682 1
0.1%
63.00212057 1
0.1%
62.96654719 1
0.1%

support_trump_score
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.027742
Minimum21.089395
Maximum93.102273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-01-31T23:31:55.721140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21.089395
5-th percentile28.692359
Q140.875254
median52.701138
Q362.815907
95-th percentile75.326254
Maximum93.102273
Range72.012877
Interquartile range (IQR)21.940653

Descriptive statistics

Standard deviation14.417022
Coefficient of variation (CV)0.2771026
Kurtosis-0.77599993
Mean52.027742
Median Absolute Deviation (MAD)10.943681
Skewness0.03788125
Sum58635.265
Variance207.85053
MonotonicityNot monotonic
2023-01-31T23:31:55.820230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.61280738 1
 
0.1%
62.39377965 1
 
0.1%
59.8646789 1
 
0.1%
77.24909747 1
 
0.1%
40.02062975 1
 
0.1%
77.00790514 1
 
0.1%
63.57959479 1
 
0.1%
52.9756192 1
 
0.1%
59.35100287 1
 
0.1%
43.15916955 1
 
0.1%
Other values (1117) 1117
99.1%
ValueCountFrequency (%)
21.08939527 1
0.1%
21.92020064 1
0.1%
21.95616171 1
0.1%
22.74872123 1
0.1%
23.52843483 1
0.1%
23.54673591 1
0.1%
23.5854251 1
0.1%
23.93156843 1
0.1%
23.94935972 1
0.1%
24.02316602 1
0.1%
ValueCountFrequency (%)
93.10227273 1
0.1%
89.38351031 1
0.1%
86.9205298 1
0.1%
84.8754386 1
0.1%
84.823401 1
0.1%
84.67074165 1
0.1%
84.66666667 1
0.1%
83.62135922 1
0.1%
82.74603175 1
0.1%
82.6773399 1
0.1%

Interactions

2023-01-31T23:31:51.444180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.469733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.774532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.078928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.303181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.566502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.794196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.045471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.282992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.573272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.910612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.260961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.569237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.224001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.529258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.554844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.862612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.162513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.389787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.651581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.872792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.131075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.371619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.661385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.006700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.352045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.661320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.310588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.618338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.640922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.955697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.250592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.480395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.743664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.956377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.219665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.461701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.752962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.108793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.442126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.751402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.398669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.703416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.721996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.046779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.343185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.564505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.825375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.035466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.317263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.548780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.843044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.196890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.531209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.836479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.481253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.796500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.812078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.146907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.439273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.657607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.913835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.127057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.408346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.643866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.944153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.297982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.631298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.930565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.571843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.886091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.896155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.250001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.523875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.752711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.999913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.220652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.493440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.735950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.040241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.399074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.722381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.407087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.656920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.968674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:33.974243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.335078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.599945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.834294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.079986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.302743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.571566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.822029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.131324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.487663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.809460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.491671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.736993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.054753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.059321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.436680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.683537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.919389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.163571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.390856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.652657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.911618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.221406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.576743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.899050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.577257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.818067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.150839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.147418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.529296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.772628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.012034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.257165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.479959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.745270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.010234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.318494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.672339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.995139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.672344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.907165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.249949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.238501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.624338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.861727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.107067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.352252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.574573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.838882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.109342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.423099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.772448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.097741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.766956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.001251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.342032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.326089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.718056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:36.948822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.200152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.442333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.659685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:42.926032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.205430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.521697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.868534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.191827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.858565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.093334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.434626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.414187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.808647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.034409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.294747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.527920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.746290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.014659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.296512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.617803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:46.962620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.288915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:49.949173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.179921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.530713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.511293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.900239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.128004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.386831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.620513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.836901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.104775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.390597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.713398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.064730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.385052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.042293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.270513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:52.618454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:34.596370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:35.986323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:37.213591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:38.472417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:39.703605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:40.920012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:43.191381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:44.478186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:45.807502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:47.158834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:48.472131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:50.129915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:31:51.353098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-31T23:31:55.916827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
probability_race_Gprobability_race_Pprobability_race_Ogenderagepartisan_scoreturnout_scoreprobability_highest_education_high_schoolsupport_tax_on_wealthy_scoresupport_progressive_taxation_scoresupport_cannabis_legalization_scoreprobability_income_over_100kprobability_children_in_householdsupport_trump_score
probability_race_G1.000-0.466-0.7500.3190.658-0.7870.798-0.521-0.771-0.749-0.6830.569-0.4140.682
probability_race_P-0.4661.0000.353-0.402-0.5620.316-0.4440.0730.4010.3450.431-0.0690.415-0.314
probability_race_O-0.7500.3531.000-0.160-0.5680.547-0.6190.3570.6100.4940.392-0.3860.322-0.448
gender0.319-0.402-0.1601.0000.489-0.1430.427-0.261-0.193-0.136-0.3290.201-0.4140.134
age0.658-0.562-0.5680.4891.000-0.5530.610-0.098-0.510-0.568-0.6430.091-0.7520.564
partisan_score-0.7870.3160.547-0.143-0.5531.000-0.5830.2540.8800.9770.866-0.4400.125-0.959
turnout_score0.798-0.444-0.6190.4270.610-0.5831.000-0.738-0.708-0.561-0.5480.748-0.3250.449
probability_highest_education_high_school-0.5210.0730.357-0.261-0.0980.254-0.7381.0000.4540.1920.136-0.937-0.006-0.048
support_tax_on_wealthy_score-0.7710.4010.610-0.193-0.5100.880-0.7080.4541.0000.8710.783-0.6250.072-0.803
support_progressive_taxation_score-0.7490.3450.494-0.136-0.5680.977-0.5610.1920.8711.0000.882-0.3910.127-0.962
support_cannabis_legalization_score-0.6830.4310.392-0.329-0.6430.866-0.5480.1360.7830.8821.000-0.3010.284-0.883
probability_income_over_100k0.569-0.069-0.3860.2010.091-0.4400.748-0.937-0.625-0.391-0.3011.0000.1430.256
probability_children_in_household-0.4140.4150.322-0.414-0.7520.125-0.325-0.0060.0720.1270.2840.1431.000-0.130
support_trump_score0.682-0.314-0.4480.1340.564-0.9590.449-0.048-0.803-0.962-0.8830.256-0.1301.000

Missing values

2023-01-31T23:31:52.750966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T23:31:52.920155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

probability_race_Gprobability_race_Pprobability_race_Ogenderagepartisan_scoreturnout_scoreprobability_highest_education_high_schoolsupport_tax_on_wealthy_scoresupport_progressive_taxation_scoresupport_cannabis_legalization_scoreprobability_income_over_100kprobability_children_in_householdsupport_trump_score
091.5431741.7087573.6170260.47195560.44761125.12516578.11461647.44354554.11032537.92704948.35297150.07100434.31844370.612807
171.2408296.68644317.0070180.43562847.24888546.75334667.86010256.79547767.30681848.91405560.91825543.73505758.35282755.883360
285.6349172.5437607.7433510.46469949.28467744.05834370.17979642.73074366.35154949.15862961.28137156.30468648.15405752.282057
379.7089053.85536712.8257450.45587449.78677134.38150976.06285439.92916458.25897442.67644356.21176965.16454553.11178759.028224
445.5961444.79700139.9812530.42260347.66041880.77343355.75575860.14339675.13302873.96010867.95094328.01725141.27385431.042588
581.8204783.8700278.2510370.48012254.86243935.65036676.94987838.28273759.71761941.41368354.15228165.18305548.14995156.610226
677.1099104.2909915.7666670.39821446.43928666.68392959.74642968.13611674.55307363.00816774.27041725.07622554.55989142.358439
786.0897622.8723706.6388500.51506755.21794051.73791278.43377732.58695764.83962355.76227263.70056163.05399737.20687242.474053
892.8383121.3061992.9141150.48655763.43764020.13517678.48581054.66679254.94246930.86288546.88617644.65815230.55522271.585274
970.1214421.88045526.0018980.48007650.64136643.11385264.39658459.83938868.49473748.19120554.49139637.54302148.01147259.030593
probability_race_Gprobability_race_Pprobability_race_Ogenderagepartisan_scoreturnout_scoreprobability_highest_education_high_schoolsupport_tax_on_wealthy_scoresupport_progressive_taxation_scoresupport_cannabis_legalization_scoreprobability_income_over_100kprobability_children_in_householdsupport_trump_score
111767.6239677.46403418.3053260.41315044.36299775.09220267.62140742.89376475.07970571.00939274.20354148.05465746.67821431.261894
111838.1355375.11767751.8180600.45920242.90188674.55498159.28082864.69052073.25034166.47550163.62822036.50479952.17107341.724701
111976.8211594.06473615.6141060.47159450.19507342.82151867.75314256.59124366.96395745.16350355.97747444.50341748.32194457.905087
112068.7358312.76215311.2101020.42417149.49407650.10071163.64763067.46144766.26376746.25567065.66913329.89138250.10790257.854858
112180.9103142.35073711.6569510.46077548.99615834.20397172.51232844.37395260.62232942.38942653.79593862.77014853.54384360.963894
112289.4057501.4890936.8245890.46506455.49815518.18803872.30765861.10298157.01671133.59394844.01863141.44376742.90865077.709011
112388.2843721.5844074.3637310.46555354.10751634.36708480.12004231.85314751.87641441.97762255.65454573.96608444.77202860.855245
112484.4302052.0844279.0376720.45206948.75311181.20719876.57652236.30462777.05687776.55285475.68017653.05775139.31678524.618034
112578.6553843.78611014.4292380.44019843.66452730.52508866.17269560.13351359.67842342.67264056.12544844.12365662.96654767.640382
112621.1876883.2617311.2544120.43733949.41562981.37253257.43390672.40283974.75814974.90505878.27684123.62333644.49955633.295918